Computational simulation platform for planning of interventional procedures

ABSTRACT

In accordance with embodiments of this disclosure, a computational simulation platform comprises a computer-implemented method that includes: generating a three-dimensional (3D) reconstruction of a vessel lumen and a surface of the vessel lumen based on invasive or non-invasive imaging; generating a mesh of the 3D reconstructed vessel lumen and surface of the vessel lumen; assigning material properties to the 3D reconstructed surface of the vessel lumen; importing design and material properties of stents and balloons; generating a mesh of a stent and balloon; positioning the meshed stent and balloon within the mesh of the 3D reconstructed vessel lumen and surface of the vessel lumen; performing balloon pre-dilation, stenting and balloon post-dilation computational simulations with the mesh of the 3D reconstructed vessel lumen and surface of the vessel lumen; and assessing stent and vessel morphometric and biomechanical measures based on the computational simulations.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit under 35 U.S.C. § 119(e) ofU.S. Provisional Application Ser. No. 62/944,054, filed Dec. 5, 2019,and titled “COMPUTATIONAL SIMULATION PLATFORM FOR PLANNING OFINTERVENTIONAL PROCEDURES,” which is incorporated herein by reference inits entirety.

TECHNICAL FIELD

The present invention generally relates to systems and methods forsimulating and planning interventional procedures.

BACKGROUND

Coronary artery disease is the leading cause of death in Westernsociety. Stents are implanted in 70-90% of the 1.3 million percutaneouscoronary interventions performed annually in the US, of which 20%involve bifurcations. Coronary bifurcations remain one of the mostchallenging lesion subsets in interventional cardiology, with a lowerprocedural success rate and increased rates of adverse cardiac events,ranging between 15-20% at 6 months to 1 year post-intervention. In lieuof the continuously increasing frequency of complex coronaryinterventions (including bifurcations), the incidence ofbifurcation-related adverse outcomes is anticipated to further increase.Even though drug-eluting stents attenuate neointimal formation,restenosis with drug-eluting stents is still significant, particularlyin bifurcation lesions compared to unbranched segments, suggesting thatthe stenting technique and the associated biomechanical environment playa dominant role in the restenosis propensity of this anatomical subset.Indeed, drug-eluting stents do not address the fundamental fluid andsolid mechanics associated with stents, which appear to significantlycontribute to stent restenosis, especially in bifurcations. Despite thegreat interest in coronary bifurcations and the multiple proposedtechnical strategies, percutaneous intervention of bifurcations remainschallenging and the ideal treatment strategy is still elusive.

Since no two bifurcations are identical, no single treatment strategyexists that can be applied to every bifurcation. The most importantissue in bifurcation interventions is selecting the most appropriatetechnique tailored to a specific bifurcation. Computational simulationshave the potential to assess the local hemodynamic microenvironment inbifurcations pre- and post-stenting, providing important insights intothe role of local biomechanical stresses on neointimal hyperplasia andstent thrombosis. The quantitative association of post-interventionhemodynamics with anatomical stent restenosis can help optimize stentingtechniques and stent design, ultimately improving clinical outcomes. Inthe modern era of robust computations, bifurcation stenting simulationsusing patient-specific anatomy and high-resolution intracoronary imagingmodalities (e.g. optical coherence tomography, intravascularultrasound), as well as realistic boundary conditions and materials(arterial wall, balloons and stents), appear to be feasible andaccurate. Different stent and balloon designs and material properties,as well as plaque material properties (e.g. fibrous, fibrofatty,calcified) can be considered in those simulations. Tailoring bifurcationstenting to the patient's specific geometry and biomechanicalenvironment might help improving clinical outcomes.

SUMMARY

A computational simulation platform for interventional proceduresplanning is disclosed. In embodiments, the computational simulationplatform comprises a computer-implemented method that includes:generating a three-dimensional (3D) reconstruction of a vessel lumen anda surface of the vessel lumen (e.g., the lumen wall and/or any plaquebuilt up on the lumen wall) based on invasive or non-invasive imaging;generating a mesh of the 3D reconstructed vessel lumen and surface ofthe vessel lumen; assigning material properties to the 3D reconstructedsurface of the vessel lumen; importing design and material properties ofstents and balloons; generating a mesh of a stent and balloon;positioning the meshed stent and balloon within the mesh of the 3Dreconstructed vessel lumen and surface of the vessel lumen; performingballoon pre-dilation, stenting and balloon post-dilation computationalsimulations with the mesh of the 3D reconstructed vessel lumen andsurface of the vessel lumen; and assessing stent and vessel morphometricand biomechanical measures based on the computational simulations.

In some embodiments of the computational simulation platform, the 3Dreconstructed vessel lumen and surface of the vessel lumen are notmeshed. In this regard, the 3D reconstructions themselves may be used toperform balloon pre-dilation, stenting and balloon post-dilationcomputational simulations.

In some embodiments of the computational simulation platform, theinvasive or non-invasive imaging comprises at least one of: coronaryangiography, intravascular ultrasound, optical coherence tomography orcomputed tomography angiography.

In some embodiments of the computational simulation platform, the designand material properties of the stents and balloons are imported based oninvasive or non-invasive imaging.

In some embodiments of the computational simulation platform, the designand material properties of the stents and balloons are imported from oneor more databases including manufacturer-provided data.

In some embodiments of the computational simulation platform, the meshedstent and balloon in their crimped state are computationally positionedand bent in the mesh of the 3D reconstructed vessel lumen and surface ofthe vessel lumen.

In some embodiments of the computational simulation platform, theballoon pre-dilation, stenting and balloon post-dilation computationsare computationally simulated using finite element analysis.

In some embodiments of the computational simulation platform, thematerial properties are assigned to the 3D reconstructed surface of thevessel lumen based on invasive or non-invasive imaging.

In some embodiments of the computational simulation platform, theassigning of the material properties to the 3D reconstructed surface ofthe vessel lumen based on invasive or non-invasive imaging includes:determining wall or plaque thickness, lumen area, plaque eccentricityand plaque constituents based on invasive or non-invasive imaging.

In some embodiments of the computational simulation platform, theassigning of the material properties to the 3D reconstructed plaquebased on invasive or non-invasive imaging further includes: dividing thevessel lumen into sequential zones of plaque material; and assigning aquarter number ranging from purely calcium plaque material to purelylipid plaque material.

In some embodiments of the computational simulation platform, thecomputer-implemented method further includes: assigning plaqueplasticity based on the material properties assigned to the 3Dreconstructed plaque.

A system for simulating and planning interventional procedures is alsodisclosed. In embodiments, the system includes one or more medicalimaging devices and one or more computer systems communicatively coupledto the one or more medical imaging devices. The one or more computersystems may be configured to: generate a three-dimensional (3D)reconstruction of a vessel lumen and a surface of the vessel lumen(e.g., the lumen wall and/or any plaque built up on the lumen wall)based on invasive or non-invasive imaging data received from the one ormore medical imaging devices; generate a mesh of the 3D reconstructedvessel lumen and surface of the vessel lumen; assign material propertiesto the 3D reconstructed surface of the vessel lumen; import design andmaterial properties of stents and balloons; generate a mesh of a stentand balloon; position the meshed stent and balloon within the mesh ofthe 3D reconstructed vessel lumen and surface of the vessel lumen;perform balloon pre-dilation, stenting and balloon post-dilationcomputational simulations with the mesh of the 3D reconstructed vessellumen and surface of the vessel lumen; and assess stent and vesselmorphometric and biomechanical measures based on the computationalsimulations.

In some embodiments of the system, the invasive or non-invasive imagingdata includes coronary angiography data, intravascular ultrasound data,optical coherence tomography data and/or computed tomography angiographydata.

In some embodiments of the system, the design and material properties ofthe stents and balloons are imported based on invasive or non-invasiveimaging data received from the one or more medical imaging devices.

In some embodiments of the system, the design and material properties ofthe stents and balloons are imported from one or more databasesincluding manufacturer-provided data.

In some embodiments of the system, the meshed stent and balloon in theircrimped state are computationally positioned and bent in the mesh of the3D reconstructed vessel lumen and surface of the vessel lumen.

In some embodiments of the system, the balloon pre-dilation, stentingand balloon post-dilation computations are computationally simulatedusing finite element analysis.

In some embodiments of the system, the material properties are assignedto the 3D reconstructed surface of the vessel lumen based on invasive ornon-invasive imaging data received from the one or more medical imagingdevices.

In some embodiments of the system, the assigning of the materialproperties to the 3D reconstructed surface of the vessel lumen based oninvasive or non-invasive imaging data includes: determining wall orplaque thickness, lumen area, plaque eccentricity and plaqueconstituents based on invasive or non-invasive imaging data.

In some embodiments of the system, the assigning of the materialproperties to the 3D reconstructed plaque based on invasive ornon-invasive imaging data further includes: dividing the vessel lumeninto sequential zones of plaque material; and assigning a quarter numberranging from purely calcium plaque material to purely lipid plaquematerial.

In some embodiments of the system, the one or more computer systems arefurther configured to: assign plaque plasticity based on the materialproperties assigned to the 3D reconstructed plaque.

This Summary is provided solely as an introduction to subject matterthat is fully described in the Detailed Description and Drawings. TheSummary should not be considered to describe essential features nor beused to determine the scope of the Claims. Moreover, it is to beunderstood that both the foregoing Summary and the following DetailedDescription are example and explanatory only and are not necessarilyrestrictive of the subject matter claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. The use of the same reference numbers in different instances inthe description and the figures may indicate similar or identical items.Various embodiments or examples (“examples”) of the present disclosureare disclosed in the following detailed description and the accompanyingdrawings. The drawings are not necessarily to scale. In general,operations of disclosed processes may be performed in an arbitraryorder, unless otherwise provided in the claims.

FIG. 1 is a block diagram illustrating a system for simulation andplanning of interventional procedures, in accordance with one or moreembodiments of this disclosure.

FIG. 2 is a flow diagram illustrating a computational simulationplatform for interventional procedures planning, in accordance with oneor more embodiments of this disclosure.

FIG. 3 is a flow diagram illustrating workflow of clinical bifurcationstenting simulations, in accordance with one or more embodiments of thisdisclosure.

FIG. 4 illustrates a process for computational simulation bench stentingin a patient-specific bifurcation, in accordance with one or moreembodiments of this disclosure.

FIG. 5 illustrates results of a comparison of stenting simulationresults against a micro computed tomography (μCT)-reconstructed model,in accordance with one or more embodiments of this disclosure.

FIG. 6 illustrates results of a qualitative comparison of computationalstenting simulation of bench bifurcation models, in accordance with oneor more embodiments of this disclosure.

FIG. 7 illustrates results of a morphometric comparison of post-stentedlumen, in accordance with one or more embodiments of this disclosure.

FIG. 8 illustrates results of a morphometric comparison of computationalstenting versus reality in clinical cases, in accordance with one ormore embodiments of this disclosure.

FIG. 9 illustrates a process for computational simulation clinicalstenting, in accordance with one or more embodiments of this disclosure.

FIG. 10 illustrates results of a morphometric comparison of post-stentedlumen, in accordance with one or more embodiments of this disclosure.

FIG. 11 illustrates results of a morphometric comparison ofcomputational stenting versus reality in clinical cases, in accordancewith one or more embodiments of this disclosure.

FIG. 12 illustrates results of a morphometric comparison of a singlesimulation cross-section with optical coherence tomography (OCT) beforeand after stenting, in accordance with one or more embodiments of thisdisclosure.

FIG. 13 illustrates results of a computational fluid dynamic study, inaccordance with one or more embodiments of this disclosure.

FIG. 14 illustrates patient-specific silicone bifurcation models and abioreactor flow circuit, in accordance with one or more embodiments ofthis disclosure.

FIG. 15 illustrates a process for 3D bifurcation reconstruction stepsand angiography processing, in accordance with one or more embodimentsof this disclosure.

FIG. 16 illustrates a process for 3D reconstruction of bifurcation lumenfrom OCT, in accordance with one or more embodiments of this disclosure.

DETAILED DESCRIPTION

The present disclosure is directed to a computational simulationplatform for interventional procedures planning. In particular, acomputational stenting platform is disclosed with reference to FIGS. 1through 16. Computational simulations can yield incremental informationto the anatomical and functional assessment of coronary artery diseasein the catheterization laboratory, guiding percutaneous interventions.Computational stenting models can reproduce controversial “what if”scenarios in a 3D environment and in a cost- and time-effective fashionto elucidate the events occurring during the stenting procedure.Computational stenting can characterize the local biomechanicalmicroenvironment pre- and post-stenting, providing a framework forbifurcation stenting optimization and generating new hypotheses that canbe tested clinically.

Several computational studies on stent simulations have been reported todate, the great majority of which have focused on idealizednon-bifurcated geometries. Limited computational studies on bifurcationstenting have been reported. These studies computationally simulated1-stent techniques only in a limited number of cases (<2) usingsimplified plaque material properties and quantitative comparisons ofthe simulations to reference vessels were not performed. This disclosurepresents a novel platform for fully computational patient-specificstenting.

FIG. 1 illustrates a system 100 for simulation and planning ofinterventional procedures, in accordance with one or more embodiments ofthis disclosure. The system 100 includes one or more computer systems102 (e.g., a computer, a plurality of computers performing differentsteps/processes, and/or a local or cloud computing cluster of computersystems working together sequentially or in parallel). The system 100further includes one or more invasive or non-invasive medical imagingdevices 110 that are communicatively coupled to the one or more computersystems 102. For example, the one or more medical imaging devices 110may be physically connected (e.g., wired) to the one or more computersystems 102, wirelessly connected (e.g., via WiFi, WLAN, Bluetooth, orthe like), and/or communicatively coupled by at least one portablestorage device (e.g., USB drive, portable hard drive, or the like) thatis configured to store data collected by the one or more medical imagingdevices 110 so that the data can be transferred to the one or morecomputer systems 102.

Examples of an invasive or non-invasive medical imaging device 110include, but are not limited to, a CT scanner, an X-ray scanner, afluoroscope, an ultrasound scanner. In embodiments, the one or moreinvasive or non-invasive medical imaging devices 110 may include anynumber or combination forgoing devices.

The one or more computer systems 102 may be configured to implement thecomputational simulation platform by performing various functions, stepsand/or operations discussed herein. In embodiments, a computer system102 (or each computer system 102 of a cluster) includes at least oneprocessor 104, memory 106 and communication interface 108.

The processor 104 provides processing functionality for at least thecomputer system 102 and can include any number of processors,microprocessors, microcontrollers, circuitry, field programmable gatearray (FPGA) or other processing systems and resident or external memoryfor storing data, executable code and other information accessed orgenerated by the computer system 102. The processor 104 can execute oneor more software programs embodied in a non-transitory computer readablemedium (e.g., memory 106) that implement techniques/operations describedherein. The processor 104 is not limited by the materials from which itis formed, or the processing mechanisms employed therein and, as such,can be implemented via semiconductor(s) and/or transistors (e.g., usingelectronic integrated circuit (IC) components), and so forth.

The memory 106 can be an example of tangible, computer-readable storagemedium that provides storage functionality to store various data and/orprogram code associated with operation of the computer system102/processor 104, such as software programs and/or code segments, orother data to instruct the processor 104, and possibly other componentsof the computer system 102, to perform the functionality describedherein. Thus, the memory 106 can store data, such as a program ofinstructions for operating the computer system 102, including itscomponents (e.g., processor 104, communication interface 108, etc.), andso forth. It should be noted that while a single memory 106 isdescribed, a wide variety of types and combinations of memory (e.g.,tangible, non-transitory memory) can be employed. The memory 106 can beintegral with the processor 104, can comprise stand-alone memory, or canbe a combination of both. Some examples of the memory 106 can includeremovable and non-removable memory components, such as random-accessmemory (RAM), read-only memory (ROM), flash memory (e.g., a securedigital (SD) memory card, a mini-SD memory card and/or a micro-SD memorycard), solid-state drive (SSD) memory, magnetic memory, optical memory,universal serial bus (USB) memory devices, hard disk memory, externalmemory, and so forth.

The communication interface 108 can be operatively configured tocommunicate with components of the computer system 102. For example, thecommunication interface 108 can be configured to retrieve data from theprocessor 104 or other devices (e.g., medical imaging devices 110, othercomputer systems 102, local/remote servers, etc.), transmit data forstorage in the memory 106, retrieve data from storage in the memory 106,and so forth. The communication interface 108 can also becommunicatively coupled with the processor 104 to facilitate datatransfer between components of the computer system 102 and the processor104. It should be noted that while the communication interface 108 isdescribed as a component of the computer system 102, one or morecomponents of the communication interface 108 can be implemented asexternal components communicatively coupled to the computer system 102via a wired and/or wireless connection. The computer system 102 can alsoinclude and/or connect to one or more input/output (I/O) devices (e.g.,via the communication interface 108), such as an input device (e.g., amouse, a trackball, a trackpad, a joystick, a touchpad, a touchscreen, akeyboard, a keypad, a microphone (e.g., for voice commands), etc.)and/or an output device (e.g., a display, a speaker, a tactile feedbackdevice, etc.). In embodiments, the communication interface 108 may alsoinclude or may be coupled with a transmitter, receiver, transceiver,physical connection interface, or any combination thereof.

It shall be understood that any of the functions, steps or operationsdescribed herein are not necessarily all performed by one computersystem 102. In some embodiments, various functions, steps or operationsmay be performed by one or more computer systems 102. For example, oneor more operations and/or sub-operations may be performed by a firstcomputer system, additional operations and/or sub-operations may beperformed by a second computer system, and so forth. Furthermore, someof the operations and/or sub-operations may be performed in parallel andnot necessarily in the order that they are disclosed herein.

FIG. 2 is a flow diagram illustrating a computational simulationplatform 200 for interventional procedures planning. In embodiments, thecomputational simulation platform 200 is embodied by acomputer-implemented method that includes the following blocks (e.g.,functions, steps and/or operations).

At block 202, invasive or non-invasive imaging data is collected for oneor more vessels (e.g., a coronary artery bifurcation or any othervasculature or portion thereof). For example, the one or more computersystems 102, via the one or more imaging devices 110, may be configuredto collect invasive imaging data (e.g., via coronary angiography,intravascular ultrasound and/or optical coherence tomography) and/ornon-invasive imaging data (e.g. via computed tomography angiography)associated with one or more vessels. Additionally, the one or morecomputer systems 102, via the one or more imaging devices 110, may beconfigured to collect invasive imaging data and/or non-invasive imagingdata associated with one or more vessels using any of the tools and/ortechniques described in the example embodiments (e.g., Examples 1-3)discussed below.

At block 204, a 3D reconstruction of at least one vessel lumen (e.g., acoronary artery bifurcation or any other vasculature or portion thereof)and a surface of the vessel lumen (e.g., the lumen wall and/or anyplaque built up on the lumen wall) is generated based on the invasive ornon-invasive imaging data collected by the one or more imaging devices110. For example, the one or more computer systems 102 may be configuredto generate a 3D reconstruction of a bifurcation lumen and wall/plaquebased on an invasive (e.g. angiography, optical coherence tomography orintravascular ultrasound) or non-invasive (computed tomographyangiography) imaging modality or any combination of these modalities.Special emphasis is put on reconstructing the true dimensions(thickness, eccentricity) of the arterial wall and plaque. Furthermore,the 3D reconstructed bifurcation is patient-specific. Additionally, theone or more computer systems 102 may be configured to generate the 3Dreconstruction of at least one vessel lumen (e.g., a coronary arterybifurcation or any other vasculature or portion thereof) and a surfaceof the vessel lumen (e.g., the lumen wall and/or any plaque built up onthe lumen wall) based on the invasive or non-invasive imaging datacollected by the one or more imaging devices 110 using any of the toolsand/or techniques described in the example embodiments (e.g., Examples1-3) discussed below.

Optionally, at block 206, a mesh of the 3D reconstructed vessel lumenand surface of the vessel lumen is generated. For example, the one ormore computer systems 102 may be configured to generate a mesh of the 3Dreconstructed vessel lumen and surface of the vessel lumen using any ofthe tools and/or techniques described in the example embodiments (e.g.,Examples 1-3) discussed below. In some embodiments of the computationalsimulation platform, the 3D reconstructed vessel lumen and surface ofthe vessel lumen are not meshed. In this regard, the 3D reconstructionsthemselves may be used to perform balloon pre-dilation, stenting andballoon post-dilation computational simulations.

At block 208, material properties are assigned to the 3D reconstructedsurface of the vessel lumen. For example, the one or more computersystems 102 may be configured to assign material properties to the 3Dreconstructed surface of the vessel lumen based on invasive ornon-invasive imaging data collected by the one or more imaging devices110. Realistic material properties can be assigned to the arterial walland plaque based on imaging data received from the one or more medicalimaging devices 110. This may include broad coverage of materials fromlipid to fibrous and calcified, including a wide range of combinationsbetween these materials. In some embodiments, the one or more computersystems 102 are configured to assign material properties to the 3Dreconstructed surface of the vessel lumen by determining wall or plaquethickness, lumen area, plaque eccentricity and plaque constituents basedon invasive or non-invasive imaging. In some embodiments, the one ormore computer systems 102 are further configured to assign materialproperties to the 3D reconstructed surface of the vessel lumen bydividing the vessel lumen into sequential zones of plaque material andassigning a quarter number ranging from purely calcium plaque materialto purely lipid plaque material. In some embodiments, the one or morecomputer systems 102 are configured to assign plaque plasticity based onthe material properties assigned to the 3D reconstructed plaque.Additionally, the one or more computer systems 102 may be configured toassign material properties to the 3D reconstructed surface of the vessellumen based on invasive or non-invasive imaging data collected by theone or more imaging devices 110 using any of the tools and/or techniquesdescribed in the example embodiments (e.g., Examples 1-3) discussedbelow.

At block 210, design and material properties of stents and balloons areimported and used to generate a mesh of a stent and balloon. The truestent design and materials, as well as realistic pre- andpost-dilatation balloon geometries with compliant, semi-compliant andnon-compliant properties are incorporated in the computational platform200. For example, the one or more computer systems 102 may be configuredto import design and material properties of the stents and balloonsbased on invasive or non-invasive imaging data received from the one ormore medical imaging devices 110. Alternatively, or additionally, theone or more computer systems 102 may be configured to import design andmaterial properties of the stents and balloons from one or moredatabases including manufacturer-provided data. The one or more computersystems 102 may be configured to generate a mesh of a stent and balloon,preferably with structured mesh, using any of the tools and/ortechniques described in the example embodiments (e.g., Examples 1-3)discussed below. The meshed stent and balloon in their crimped state maybe computationally positioned and bent in the mesh of the 3Dreconstructed vessel lumen and surface of the vessel lumen. In someembodiments, the stents and balloons are bent and positioned in the meshof the lumen following the true 3D course of the artery. Additionally,the one or more computer systems 102 may be configured to import designand material properties of the stents and balloons, generate a mesh of astent and balloon, and/or position the mesh of the stent and balloonwithin or relative to the mesh of the 3D reconstructed vessel lumen andsurface of the vessel lumen using any of the tools and/or techniquesdescribed in the example embodiments (e.g., Examples 1-3) discussedbelow.

At block 212, computational stent simulations are performed. Forexample, the one or more computer systems 102 may be configured toperform balloon pre-dilation, stenting and balloon post-dilationcomputational simulations with the mesh of the 3D reconstructed vessellumen and surface of the vessel lumen. In some embodiments, the balloonpre-dilation, stenting and balloon post-dilation computations arecomputationally simulated using finite element analysis. Preferably,realistic inflation pressures are used. The preload of each step may bethe baseline for the next step, thereby causing the lumen and wallexpansion to follow realistic patterns. Additionally, the one or morecomputer systems 102 may be configured to perform balloon pre-dilation,stenting and balloon post-dilation computational simulations with themeshed structures using any of the tools and/or techniques described inthe example embodiments (e.g., Examples 1-3) discussed below.

At blocks 214 through 218, stent and vessel morphometric andbiomechanical measures are assessed based on the computationalsimulations. For example, the one or more computer systems 102 may beconfigured to assess morphometric measures including, but not limitedto, stent expansion and apposition. The one or more computer systems 102may be further configured to assess biomechanical measures (e.g.,hemodynamic measures) including, but not limited to, fluid and solidstresses in the arterial lumen, wall and stent using computational fluiddynamics and finite element analysis. Additionally, the one or morecomputer systems 102 may be configured to assess stent and vesselmorphometric and biomechanical measures based on the computationalsimulations using any of the tools and/or techniques described in theexample embodiments (e.g., Examples 1-3) discussed below.

The proposed computational stenting platform 200 can yield incrementalinformation to the anatomical and functional assessment of coronaryartery disease in the Cath Lab, guiding the percutaneous interventions.Patient-specific computational stenting models can reproducecontroversial “what if” scenarios in a 3D environment and in a cost- andtime-effective fashion to elucidate the events occurring during thestenting procedure. These models can characterize the localbiomechanical microenvironment pre- and post-stenting, providing aframework for stenting optimization and generating new hypotheses thatcan then be tested clinically. In the era of powerful computers,predictive patient-specific computational simulations of bifurcationstenting are feasible and reliable.

Computational simulations of bifurcation stenting can help the industryto develop new generation stents, ultimately improving clinicaloutcomes. Stenting simulations have the potential to be used (near) realtime to guide bifurcation PCIs (auto-pilot PCI), which might beparticularly useful for non-expert interventionalists. Thepatient-specific computational stenting approach integrates thebifurcation anatomy, disease complexity and biomechanics in acomprehensive scheme. This allows for the generation of a comprehensiveatlas of patient bifurcations and simulations of various stentingtechniques applied to the entire range of real-world bifurcationgeometries. Such a patient bifurcation atlas may help identify favorablestenting techniques for specific groups of bifurcation anatomy and setthe stage for real-time decision making in the Cath Lab using machineand deep learning strategies. Also, the proposed computational platformmay evolve to a helpful educational and training tool, and finally canbe applied to other vascular beds (i.e. carotid artery bifurcation oraortic bifurcation or structural heart disease interventions).

Specific implementations of the computational simulation platform 200are discussed in the examples and embodiments (e.g., Examples 1-3)discussed below. The following examples and embodiments should not beconstrued as limitations of the present disclosure, unless otherwisespecified in the Claims. In other implementations, equivalent systems,tools, materials, software and/or processes may be employed withoutdeviating from the scope of this disclosure. Furthermore, certainaspects or configurations described with respect to a specificembodiment may be implemented in combination with any aspect orconfiguration of another embodiment without deviating from the scope ofthis disclosure.

EXAMPLE 1 Patient-Specific Bench Stenting

Silicone bifurcation models: Four bench models of patient-specificcoronary artery bifurcations were created using an in-house technique.Specifically, the initial bifurcation geometries were 3D reconstructedfrom human coronary angiograms. For each model, a negative mold wasdesigned and 3D printed with acrylonitrile butadiene styrene (ABS)material. After smoothing the inner surface of the mold using acetonevapor, polydimethylsiloxane (PDMS) was injected into the mold and thenput in the oven for curing of PDMS. The physical models were thenimmersed in an acetone beaker to dissolve the ABS and generate thesilicone bifurcation models for bench test.

Contrast enhanced micro computed tomography (μCT) imaging: To acquirehigh-resolution lumen geometry, all silicone bifurcation models werefilled with contrast, and then scanned with μCT (e.g., SkyScan 1172version 1.5, SKYSCAN, Antwerpen, Belgium) using the followingparameters: Image pixel size (26.9 μm), voltage (100 kV), and current(100 μA). The reconstructed 3D models based on μCT before stentingserved as anatomical input to computational stent simulations.

Bench stenting of silicone bifurcation models: The silicone bifurcationmodels were placed in a custom-made flow chamber. A computer-controlledbioreactor circuit was connected to the inlet and outlets of thebifurcation, allowing circulation of 1 L of deionized water at a steadyflow-rate of 100 ml/min.

Stereoscopic scanning: All the stents deployed in the siliconebifurcation models were imaged with a stereo microscope (e.g., OlympusSZX16, Tokyo, Japan). The microscopic images were used to measure thedistance of the stent edges from fixed points (e.g., carina) and guidethe correct positioning of the stents in the computational models.

Computational mesh: The 3D reconstructed lumens by μCT were meshed withfour-node quadrilateral shell elements using HyperMesh (AltairEngineering, Troy, Mich., USA). The computer-aided design models ofstents used in the bench stenting procedures were provided by themanufacturers (Boston Scientific, Maple Groove, Minn., USA and MedtronicVascular, Santa Rosa, Calif., USA) at their nominal dimensions. Theballoons were computationally created in Grasshopper (plugin toRhinoceros 6.0, Robert McNeel and Associates, Seattle, Wash., USA) attheir crimped state. The stents were meshed in HyperMesh using beamelements (Resolute Integrity and Onyx. Medtronic) or hexahedral elements(Synergy, Boston Scientific), whereas the balloons were meshed withquadrilateral finite-membrane-strain elements.

Material properties: The cured silicone samples were cut intorectangular specimens and underwent uni-axial compression testing. Theobtained force-displacement curves were converted into strain-stresscurves. The Neo-Hookean hyperelastic model was used to fit thenon-linear strain-stress curve. A specific thickness was assigned to theshell elements of each bifurcation to represent the true thickness ofthe silicone models. The elastic modulus for compliant, semi- andnon-compliant balloons was defined as 300 MPa, 900 MPa and 1,500 MPa,respectively.

Stent and balloon crimping, positioning, and bending: The correct stentand balloon positioning in the computational bifurcation models wasdetermined by angiography, μCT and stereoscopic images of the stentedsilicone models. The stents were first crimped from their nominal statesby using surface elements driven by radial displacement. The crimpedstents and balloons were positioned and bent along the arterycenterline.

Computational simulation of bench stenting procedures: The benchstenting procedures were simulated through a multi-step, quasi-staticfinite element analysis using the central difference method(Abaqus/Explicit solver). The edges of the bifurcation lumen were fixedto avoid rigid body motion. The balloon edges were constrained toeliminate their motion in all the directions. To model the interactionsbetween different elements (balloon-stent, stent-lumen, balloon-lumen,balloon-balloon), the robust general contact algorithm was used with afriction coefficient of 0.2. The real inflation pressures, which wereused in each procedural step, were applied onto the inner surface of thecorresponding balloons. The stressed configurations of lumen and stentafter each step were used as the initial condition for the next step.Given the very large number of elements and complicated contacts in thecomputational model, a computer cluster (452 Intel Xeon E5-2670 2.60 GHz2 CPU/16 cores and 64 GB RAM per node University of Nebraska) was usedto perform the high-speed computational simulations.

Training of computational bench stenting: The 3D reconstructedbifurcation and stent geometry by μCT post-stenting served as groundtruth for the training of computational stenting. The simulatedbifurcation and μCT bifurcation were co-registered using the bifurcationcarina as fixed point. The mean lumen diameter (MLD) was used for thecomparison studies.

Results: All bench stenting procedures, the majority of which weremulti-step two stent techniques, were successfully simulated. FIG. 4illustrates a representative example of T-and-Protrusion (TAP) techniquewith two stents. Visually, the computationally simulated stents werenearly identical in size and shape to the actual μCT-reconstructedstents (FIG. 5, (a)). The MLD was plotted along the axial direction ofthe simulated and μCT reconstructed stents (FIG. 5, (b)), andquantitatively compared between methods with Bland Altman analysis thatyielded a minimal mean difference of 0.02 (−0.12 to 0.17) mm.

Contrast enhanced μCT and stereoscopic images further revealed theability of the disclosed computational stenting platform to replicatewith high precision fine details of the bench stenting procedures,including malapposed struts, side branch ostium size and shape, and gapsin struts around the anatomically sensitive site of carina (FIG. 6).

EXAMPLE 2 Clinical Stenting

Patient data: Seven patient cases were selected for the patient-specificcomputational simulations from the PROPOT (Randomized trial of theproximal optimization technique in coronary bifurcation lesions, IRBapproval number 15-159-2), a multi-center, prospective, open-label studythat compared proximal optimization technique vs. kissing ballooninflation in provisional stenting of coronary bifurcations usingZotarolimus-eluting stents (Resolute Integrity or Onyx, Medtronic). Allpatients underwent coronary angiography at multiple angiographic planesand intracoronary imaging with OCT of main vessel (MV) and side branch(SB) before percutaneous coronary interventions (PCI), immediately poststenting, and at the end of the procedure. Pre-PCI anatomical imagingdata were used to 3D reconstruct the patient-specific coronarybifurcation anatomies which served as anatomical input to thecomputational stenting simulations. The workflow for computationalsimulations of clinical bifurcation stenting is illustrated in FIG. 3.

Training group: Five out of seven cases were used for training of thecomputational stenting platform, wherein none of the operators wereblinded. All the PCI steps performed in this group of cases werereplicated in the computational environment. The post-PCI OCT data wereused as ground truth for the comparison to the computationalsimulations.

Testing group: Two cases were chosen for testing of the computationalstenting platform. The operators responsible for angiography and OCTimaging analysis, 3D reconstruction of vessels, computational stentingsimulations and comparison studies were blinded to each other. Thestenting simulation results were compared with the post-PCI OCT imaging.

3D reconstruction of bifurcation geometry: The pre-PCI bifurcationgeometries were 3D reconstructed from fusion of angiography and OCT, aspreviously described. Briefly, the bifurcation centerline was generatedfrom two angiographic planes (Pie Medical Imaging BV, Maastricht,Netherlands), and served as the backbone of the reconstruction. Thesegmented OCT images (EchoPlaque 4.0, Indec, Los Altos, Calif., USA)were aligned along the centerline using the carina as reference point. Astep-wise approach was followed for the delineation of the outer bordersin OCT images, as previously described. The disclosed approach workedsuccessfully in >95% of images and involved the following steps: (i) incase of ill-defined outer wall borders, the outer wall was limited atthe margin of the complete signal loss; (ii) in case the margin ofcomplete signal loss could not be identified in <180 degrees of vesselcircumference, the visible outer wall border was interpolated; and (iii)in case the margin of complete signal loss could not be identifiedin >180 degrees of vessel circumference, that particular OCT frame wasdiscarded and an adjacent frame was segmented following the same steps(i-ii). The aligned lumen and wall contours were lofted to build the MVand SB inner and outer surfaces, and the MV and SB surfaces were finallymerged to create the pre- and post-PCI bifurcation lumen and wall.

Computational mesh: The 3D reconstructed bifurcation models were meshedwith hexahedral elements (ICEM CFD 17.2, ANSYS, Inc., Canonsburg, Pa.,USA). The stent design models were provided by the manufacturer(Medtronic Vascular, Santa Rosa, Calif., USA) at their nominaldimensions. The balloons were constructed in Grasshopper at theircrimped state. The stents and balloons were meshed using fullyhexahedral and quadrilateral finite-membrane-strain elements,respectively.

Material properties: In computational simulations, the wall thickness,lumen area, plaque eccentricity and plaque material were determined byOCT. A novel plaque scoring system was established based on theexperimental data represented by the stress/strain graph of sixth orderpolynomial coefficients. The area, circumference and thickness of lipid,fibrous and calcified material were assessed in each OCT frame of MV andSB pullback by an imaging expert (YSC). Of note the imaging expert wasblind to the simulation results of the testing group. Then, the MV andSB were divided into sequential zones of homogeneous plaque material.Each zone was assigned a quarter number (e.g. −0.25, 0, +0.25, etc.)ranging from +2 (calcium only) to −2 (lipid only). Fibrolipid plaqueswith predominant lipid were assigned a score of −0.0.75 or −0.5,fibrolipid plaques with necrotic core −0.25, fibrous plaques 0,fibrolipid plaques with predominant fibrous +0.25, fibrocalcific plaquewith moderate calcium +0.5, fibrocalcific plaque with severe calcium+0.75 or +1. Normal wall thickness and tapering were assessed by OCT.The normal wall material was modeled using sixth-order reducedpolynomial constitutive equation to characterize the isotropichyper-elastic mechanical behavior, as previously described. Thecoefficients for arterial media layer were obtained by fitting theequation to the experimental data. Wall plasticity ranging from 19-25%was assigned based on the material properties. The cobalt alloy MP35N ofResolute Integrity and Onyx was modeled with the Von Mises-Hillplasticity model with isotropic hardening, while the Pt-Ir alloy core ofResolute Onyx was modeled with perfect plasticity. The balloons weremodeled as pure linear elastic material with the same materialproperties as in the simulations of bench group.

Stent and balloon crimping, positioning, and bending: All stents werefirst crimped from their nominal states using surface elements driven byradial displacement. The crimped stents and balloons were positioned andbent along the centerline (FIG. 3). The stent and balloons wereprecisely positioned in the bifurcations by referring to fiduciarymarkers (e.g., radiopaque markers of stent/balloons, carina, andintersection points of guidewires) on the angiography and OCT.

Computational simulations: All the steps of PCI procedures werecomputationally replicated through a multi-step, large-deformation,quasi-static finite element analysis using the central difference method(Abaqus/Explicit solver). The boundary conditions and simulationparameters are described above. The computer cluster above was used toperform the computational simulations.

3D stent reconstruction from OCT: The stents were 3D reconstructed fromOCT and angiographic images using a custom-built Grasshopper Pythoncode. First, the stent struts were segmented as individual points andflattened to 2D surfaces. Using the 2D stent design pattern asreference, the stent points were connected by lines that represented thecenterlines of stent struts and links. The 2D stent centerlines werewrapped and mapped back to the 3D lumen centerline, and then the volumeof stent struts was added.

Computational fluid dynamic (CFD) studies: The post-computational PCIbifurcation geometries were used to discretize the fluid domain for CFDanalyses (FIG. 3). The fluid domain was meshed with tetrahedral elementsusing ICEM CFD (ANSYS Inc., Canonsburg, Pa., USA). Transient CFDsimulations were performed by means of Fluent (ANSYS Inc.). Pulsatileflow was applied at the inlet of each artery. The Huo-Kassab (HK) lawwas used to derive the relation between the diameter ratio of twodaughter branches and the flow ratio through the branches. The lumen andstent surfaces were approximated as rigid body, where non-slip boundaryconditions were applied. The blood density was considered constant witha value of 1,060 kg/m³.The Carreau model was adopted to consider thenon-Newtonian nature of blood. The following values for each parameterwere used: μ_(∞)=0.0035 Pa·s, μ₀=0.25 Pa·s, λ=25 s and n=0.25. One fullcardiac cycle, divided into 100 time steps with a step time of 0.009 s,was simulated.

Comparison metrics: The final lumen and stent geometries aftercomputational stenting simulations were compared to the lumen and stentcross-sections segmented on post-PCI OCT. The cross-sections frompost-PCI OCT were used as reference. The simulated bifurcation and framenumber of OCT cross-sections were co-registered using carina as fixedmarker. The MLD along the stented MV and the mean stent diameter (MSD)were used as comparison metrics.

Statistical methods: Statistical analysis was performed with thestatistical package GraphPad Prism 8.0 (GraphPad Inc., San Diego,Calif., USA). Continuous variables were expressed as mean±standard errorof mean. Bland-Altman analysis was used for comparison, p value of <0.05was considered statistically significant.

Training group results: In the training group, clinical stentingprocedures, all of which were performed with one stent technique, weresuccessfully simulated with our computational platform. Visually, thecomputationally stented bifurcation lumen yielded high qualitativeagreement with the angiographic lumen post stenting (FIG. 7). BlandAltman analysis revealed MLD differences close to zero [mean bias 0.07mm (−0.31 to 0.45) mm]. Similarly, the computationally simulated stentsexhibited high similarity to the shape and size of the actual stentswhich were 3D reconstructed by fusing OCT and angiography (FIG. 8, (a)and (b)). The MSD of 16 the computationally simulated stents wasquantitatively compared to the OCT stent segmentations, yielding veryhigh agreement [mean bias 0.14 mm (−0.22 to 0.49) mm]. Notably, inPatient #1, the computational simulation replicated the stentunder-expansion around the carina secondary to the local stiff plaquematerial. In Patient #5, the computational stenting reproduced the largegaps between stent struts and consequent over-dilated lumen at theproximal MV following the proximal stent post-dilatation.

Testing group results: In the testing group, the pre-proceduralanatomical information (angiography and OCT) was used to assess theability of the disclosed computational platform to replicate theclinical stenting (FIG. 9). The computational simulation operators wereblinded to the post-procedural OCT. As shown in FIGS. 10 and 11, thecomputational stenting yielded very high agreement to post-proceduralOCT suggesting the robustness of our platform. Quantitative comparisonsby Bland-Altman showed small differences in MLD and MSD [mean bias 0.09mm (−0.29 to 0.46) and 0.13 mm (−0.24 to 0.49) mm, respectively. FIG. 12provides an example of blinded use of pre-procedural OCT to incorporatethe anatomical information of lumen area, plaque thickness andeccentricity into our computational platform and also assignpatient-specific material properties in order to achieve realisticcomputational simulations that yielded similar lumen and stent expansionwith the post-procedural OCT.

CFD studies: To show the feasibility of CFD in the disclosed simulatedprocedures, the time-averaged wall shear stress (TAWSS) along the axialdirection of MV and SB was compared before and after computationalstenting (FIG. 13). As shown quantitatively and qualitatively, stentingnormalized the TAWSS in MV.

The proposed computational platform (i.e., computational simulationplatform 200) for patient-specific bifurcation stenting simulationprovides a reliable resource for clinical research, clinical decisionmaking, stent manufacturing and education on stenting techniques. Morespecifically, computational stenting can be used in virtual (in-silico)clinical trials using patient-specific anatomical and physiology dataand surrogate endpoints (i.e. under-expansion, malapposition, flowdynamics) highly predictive of clinical endpoints. These virtualclinical trials can be adequately powered with large patient data toinvestigate the performance of different stenting techniques or stentplatforms, thereby guiding the actual clinical trials. Flow ISR which iscurrently underway is an example of such a virtual clinical trial. Thestudy compares different 1- and 2-stent techniques in patient-specificcoronary bifurcations. In cardiac catheterization laboratory,computational stenting simulations can be used for pre-proceduralplanning and decision-making. Computational identification of theoptimal stenting and post-dilatation technique that secures the mostfavorable stent expansion and apposition, as well as hemodynamicmicroenvironment can provide invaluable guidance to theinterventionalist, and possibly increase the procedural success andlong-term clinical outcomes (precision medicine). When it comes to stentmanufacturers, a cost- and time-effective computational stentingstrategy with patient-specific anatomies has the potential to minimizethe need for bench and animal research for stent testing. Computationalsimulations can help with optimization of stent design (e.g. number ofcrowns and links, strut size) and mechanics (radial and longitudinalstrength, expansion capability, vessel scaffolding). The computationalapproach can effectively evaluate different stent designs in realisticvessel environments obviating the need to manufacture and experimentallytest stent prototypes, significantly reducing the development time andmanufacturing costs. Another important consideration with computationalstenting is that it can be used as an educational tool to train staffand physicians on bifurcation stenting techniques. Mixed realitytechnologies can further assist towards this direction. Finally,computational bifurcation stenting can be translated to other vascularbeds (e.g. carotid, renal or aortic bifurcations).

Coronary artery bifurcations represent unique anatomical locations inthe epicardial coronary tree with increased susceptibility to coronaryartery disease. Specific anatomic features of bifurcations, includingthe angle and diameter of the main vessel (MV) and side branch (SB),have a significant impact on the local hemodynamic milieu and subsequentpropensity to atherosclerosis. The bifurcation anatomy and extent ofdisease are substantial determinants of bifurcation stenting strategiesand clinical outcomes. Three-dimensional (3D) representation of thebifurcation anatomy and disease burden could help us better appreciatethe anatomical complexity of bifurcation disease and optimize thedisclosed stenting strategies.

Dedicated single-modality 3D reconstruction of coronary bifurcations canbe performed with either 3D quantitative coronary angiography (3D QCA)or coronary computed tomography angiography (CTA). However, both thesemodalities have major limitations: 3D QCA cannot provide the correctgeometrical information of the bifurcation lumen due to the inherentassumptions related to the use of two 2D angiographic planes.Nevertheless, 3D QCA provides accurate details on the 3D course of thebifurcation centerline. Coronary CTA is limited by heart and lung motionartifacts and coronary calcifications, resulting in the exclusion of adescent portion of patients. Hybrid multi-modality 3D reconstruction ofbifurcations based on the fusion of intravascular ultrasound (IVUS) oroptical coherence tomography (OCT) of the MV only with coronary CTA orinvasive angiography has been described. These approaches havelimitations mostly related to the accuracy of SB reconstruction.Notably, the use of different imaging modalities for MV and SBreconstruction results in inaccuracies in the reconstruction of thegeometrically sensitive and clinically important bifurcation carina andSB. Also, using invasive imaging (IVUS) for the reconstruction of MV andnon-invasive imaging (CTA) for the reconstruction of SB is not easilyapplicable in the clinical setting.

This disclosure builds upon the current state-of-the-art and proposes anovel strategy for the 3D reconstruction of coronary bifurcations basedon the fusion of invasive coronary angiography—which provides thebifurcation centerline—with OCT of both MV and SB. Studies describedherein were performed: (i) to describe the methodology for 3Dreconstruction of coronary bifurcations; and (ii) to systematically testthe accuracy, feasibility, and reproducibility of the method inpatient-specific silicone bifurcation models, as well as in patientcoronary artery bifurcations with varying degrees of disease.

EXAMPLE 3 3D Reconstruction of Coronary Artery Bifurcations

Silicone models: Five patient-specific silicone models of coronaryartery bifurcations were 3D reconstructed, using the disclosedalgorithm. The bifurcation geometries were 3D reconstructed from humancoronary angiograms during the diastolic phase of the cardiac cycle,using commercially available software (3D CAAS Workstation 8.2, Piemedical imaging, Maastricht, The Netherlands). A flow diagram of theprocess is illustrated in FIG. 14, (a). To demarcate the region ofinterest and stabilize the silicone models during the imagingprocedures, tube-like extensions and fixed markers were added at theinlet and outlet of the reconstructed bifurcations using acomputer-aided design software (Rhinoceros 6, Robert McNeel &Associates, Seattle, USA). For every model, a negative mold was designedand converted to stereolithography (STL) files. The STL files were 3Dprinted with acrylonitrile butadiene styrene material using theStratasys Dimension Elite 3D printer (Stratasys, Rehovot, Israel) at aresolution of 178 μm. Acetone vapor was used to produce a smooth innersurface. The molds were stored in room temperature for 8-12 hours andcleaned with distilled water and dried. Polydimethylsiloxane was mixedwith its curing agent and then placed into a vacuum for a total of 1hour and 30 minutes to remove the air bubbles. Subsequently,polydimethylsiloxane was poured into the dry clean molds, which wereplaced in the vacuum to remove any remaining air bubbles and then put inthe oven for polydimethylsiloxane curing for 48 hours at the temperatureof 65° C. After curing, the silicone models were put in an acetonebeaker, which was placed in an ultrasonic cleaner (Branson 1800,Cleanosonic, Va., USA) for 8-10 hours to dissolve all acrylonitrilebutadiene styrene material.

Contrast-enhanced micro-computed tomography (μCT) imaging: All thebifurcation models were imaged with μCT (Skyscanner 1172 version 1.5)using the following parameters: image pixel size 26.94 μm, voltage 100kV, current 100 μA, and slice thickness 27 μm. To visualize the lumenborders effectively, iodinated contrast media (37%) was injected intothe lumen. The bifurcations were 3D reconstructed from the μCT imagesusing a 3D medical imaging software (Materialise Mimics 22.0,Materialise, Leuven, Belgium) and smoothened using Meshmixer (AutodeskResearch, New York, N.Y.).

Bioreactor flow circuit for invasive imaging procedures: Thesilicone-based bifurcation models were placed in a custom-made flowchamber. Polyvinyl chloride tubing was connected at the inlet and outletports of the silicone models. A bioreactor circuit was connected to theinlet and outlet of the flow chamber, allowing circulation of 1,000 mlof deionized water at a steady flowrate of 100 ml/min at roomtemperature (FIG. 14, (b)). All the bifurcation models were imaged withangiography and OCT imaging of both the MV and SB.

3D QCA for 3D reconstruction of bifurcation centerline: The flowchartfor the 3D reconstruction of the bifurcation model is shown in FIG. 15,(a), and the detailed steps in FIGS. 15, (b) and (c) and FIG. 16.Angiography of the bifurcation models was performed at two projectionswith at least 30° difference in viewing angles (FIG. 16, (b)). In eachprojection, the lumen of the segment of interest was manually detected,and the bifurcation carina was set as a common reference location (e.g.,carina reference). The 3D replica of the bifurcation models was createdin CAAS and exported to VMTK (Orobix, Bergamo, Italy) for the extractionof MV and SB centerlines. On each centerline, a carina point can befound according to the carina reference projecting to the centerline(FIG. 16, (c)).

Acquisition and segmentation of OCT: OCT imaging of the MV and SB wasobtained using the OPTIS Integrated System (Abbott, Chicago, Ill., USA;FIG. 16, (a)). The OCT catheter (Dragonfly, Optis Imaging Catheter) wasadvanced through a 6F guiding catheter and pulled back (automatictriggering by saline without contrast) at a speed of 36 mm/sec (5frames/mm) for 75 mm, covering the entire length of MV and SB from thedistal to the proximal fixed marker (FIG. 14, (a)). Lumen segmentationof the OCT frames was carried out semi-automatically using echoPlaque4.0 (INDEC Medical Systems, Los Altos, Calif., USA; FIG. 16, (b)).

OCT processing for bifurcation lumen reconstruction: The detailed stepsof the bifurcation lumen reconstruction are illustrated in FIG. 16.Briefly, the segmented OCT frames were imported into Grasshopper 3D(visual programming language and environment that runs within theRhinoceros 3D) and packaged in a straight line along the catheter center(FIG. 16, (c)). The OCT frame misalignment was corrected with anin-house script (FIG. 16, (d) and (e)). The correctly aligned OCT frameswere positioned perpendicularly on the respective bifurcation centerlinewith the centerline passing through the centroid of every frame (FIG.16, (f)). In particular, the OCT frame at the carina was positioned atthe carina point (point A in FIG. 16, (f)), and the rest of the frameswere positioned in a specific location along the centerline according tothe known distance between them. The frames were then rotated to alignwith the carina reference (point C in FIG. 16, (g)). The primarysurfaces of MV and SB were created and served as a reference for thecreation of a final uniform, smooth, and continuous bifurcation surfaceusing the method of T-spline (FIG. 16, (h)).

Additional details are discussed in Wu, W. et al., “3D Reconstruction ofCoronary Artery Bifurcations from Coronary Angiography and OpticalCoherence Tomography: Feasibility, Validation, and Reproducibility,”Scientific Reports (2020), which is incorporated herein by reference inits entirety.

The disclosed methodology has several clinically important applications.The 3D reconstructed bifurcation can inform the proceduralists about theprecise bifurcation anatomy, as well as the extent and severity ofcoronary artery disease. A better understanding of the disease burdencan result in better procedural planning and outcomes. Moreover, the 3Dreconstructed bifurcation lumen itself can be used for computational andexperimental (bench) fluid dynamics studies to explore the role of flowin native coronary artery disease development and progression, as wellas in-stent restenosis and thrombosis. The disclosed methodologyprovides the accurate geometrical input needed for realisticcomputational fluid dynamic studies. The disclosed technique can createthe basis for finite element analysis and patient-specific computationalbifurcation stenting simulations.

Furthermore, computational stenting simulations using patient-specificbifurcation anatomy and plaque properties, as well as realistic stentgeometry, can provide personalized planning of stenting techniques.Patient-specific bifurcation anatomies are also particularly relevant tothe industry for the testing and development of new generation stents.Finally, the basic principles of the disclosed methodology can betranslated to other invasive imaging modalities, e.g., IVUS or evennon-invasive imaging, e.g., coronary CTA. As long as there is imagingdata available to extract the lumen centerline and lumen/vessel wallborders, the disclosed methodology has the potential to perform well.

Although the technology has been described with reference to theembodiments illustrated in the attached drawing figures, equivalents maybe employed and substitutions made herein without departing from thescope of the technology as recited in the claims. Components illustratedand described herein are examples of devices and components that may beused to implement the embodiments of the present invention and may bereplaced with other devices and components without departing from thescope of the invention. Furthermore, any dimensions, degrees, and/ornumerical ranges provided herein are to be understood as non-limitingexamples unless otherwise specified in the claims.

1. A computational simulation platform for interventional proceduresplanning, comprising a computer-implemented method that includes:generating a three-dimensional (3D) reconstruction of a vessel lumen anda surface of the vessel lumen based on invasive or non-invasive imaging;generating a mesh of the 3D reconstructed vessel lumen and surface ofthe vessel lumen; assigning material properties to the 3D reconstructedsurface of the vessel lumen; importing design and material properties ofstents and balloons; generating a mesh of a stent and balloon;positioning the meshed stent and balloon within the mesh of the 3Dreconstructed vessel lumen and surface of the vessel lumen, wherein themeshed stent and balloon in their crimped state are computationallypositioned and bent in the mesh of the 3D reconstructed vessel lumen andsurface of the vessel lumen; performing balloon pre-dilation, stentingand balloon post-dilation computational simulations with the mesh of the3D reconstructed vessel lumen and surface of the vessel lumen; andassessing stent and vessel morphometric and biomechanical measures basedon the computational simulations.
 2. The computational simulationplatform of claim 1, wherein the invasive or non-invasive imagingcomprises at least one of: coronary angiography, intravascularultrasound, optical coherence tomography or computed tomographyangiography.
 3. The computational simulation platform of claim 1,wherein the design and material properties of the stents and balloonsare imported based on invasive or non-invasive imaging.
 4. Thecomputational simulation platform of claim 1, wherein the design andmaterial properties of the stents and balloons are imported from one ormore databases including manufacturer-provided data.
 5. (canceled) 6.The computational simulation platform of claim 1, wherein the balloonpre-dilation, stenting and balloon post-dilation computations arecomputationally simulated using finite element analysis.
 7. Thecomputational simulation platform of claim 1, wherein the materialproperties are assigned to the 3D reconstructed surface of the vessellumen based on invasive or non-invasive imaging.
 8. The computationalsimulation platform of claim 7, wherein the assigning of the materialproperties to the 3D reconstructed surface of the vessel lumen based oninvasive or non-invasive imaging includes: determining wall or plaquethickness, lumen area, plaque eccentricity and plaque constituents basedon invasive or non-invasive imaging.
 9. The computational simulationplatform of claim 8, wherein the assigning of the material properties tothe 3D reconstructed plaque based on invasive or non-invasive imagingfurther includes: dividing the vessel lumen into sequential zones ofplaque material; and assigning a quarter number ranging from purelycalcium plaque material to purely lipid plaque material.
 10. Thecomputational simulation platform of claim 9, wherein thecomputer-implemented method further includes: assigning plaqueplasticity based on the material properties assigned to the 3Dreconstructed plaque.
 11. A system for simulation and planning ofinterventional procedures, comprising: one or more medical imagingdevices; one or more computer systems communicatively coupled to the oneor more medical imaging devices, the one or more computer systems beingconfigured to: generate a three-dimensional (3D) reconstruction of avessel lumen and a surface of the vessel lumen based on invasive ornon-invasive imaging data received from the one or more medical imagingdevices; generate a mesh of the 3D reconstructed vessel lumen andsurface of the vessel lumen; assign material properties to the 3Dreconstructed surface of the vessel lumen; import design and materialproperties of stents and balloons; generate a mesh of a stent andballoon; position the meshed stent and balloon within the mesh of the 3Dreconstructed vessel lumen and surface of the vessel lumen, wherein themeshed stent and balloon in their crimped state are computationallypositioned and bent in the mesh of the 3D reconstructed vessel lumen andsurface of the vessel lumen; perform balloon pre-dilation, stenting andballoon post-dilation computational simulations with the mesh of the 3Dreconstructed vessel lumen and surface of the vessel lumen; and assessstent and vessel morphometric and biomechanical measures based on thecomputational simulations.
 12. The system of claim 11, wherein theinvasive or non-invasive imaging data comprise at least one of: coronaryangiography data, intravascular ultrasound data, optical coherencetomography data or computed tomography angiography data.
 13. The systemof claim 11, wherein the design and material properties of the stentsand balloons are imported based on invasive or non-invasive imaging datareceived from the one or more medical imaging devices.
 14. The system ofclaim 11, wherein the design and material properties of the stents andballoons are imported from one or more databases includingmanufacturer-provided data.
 15. (canceled)
 16. The system of claim 11,wherein the balloon pre-dilation, stenting and balloon post-dilationcomputations are computationally simulated using finite elementanalysis.
 17. The system of claim 11, wherein the material propertiesare assigned to the 3D reconstructed surface of the vessel lumen basedon invasive or non-invasive imaging data received from the one or moremedical imaging devices.
 18. The system of claim 17, wherein theassigning of the material properties to the 3D reconstructed surface ofthe vessel lumen based on invasive or non-invasive imaging dataincludes: determining wall or plaque thickness, lumen area, plaqueeccentricity and plaque constituents based on invasive or non-invasiveimaging data; dividing the vessel lumen into sequential zones of plaquematerial; and assigning a quarter number ranging from purely calciumplaque material to purely lipid plaque material.
 19. The system of claim18, wherein the one or more computer systems are further configured to:assign plaque plasticity based on the material properties assigned tothe 3D reconstructed plaque.
 20. A computational simulation platform forinterventional procedures planning, comprising a computer-implementedmethod that includes: generating a three-dimensional (3D) reconstructionof a vessel lumen and a surface of the vessel lumen based on invasive ornon-invasive imaging; generating a mesh of the 3D reconstructed vessellumen and surface of the vessel lumen; assigning material properties tothe 3D reconstructed surface of the vessel lumen; importing design andmaterial properties of stents and balloons; generating a mesh of a stentand balloon; positioning the meshed stent and balloon within the 3Dreconstructed vessel lumen and surface of the vessel lumen; performingballoon pre-dilation, stenting and balloon post-dilation computationalsimulations with the 3D reconstructed vessel lumen and surface of thevessel lumen; and assessing stent and vessel morphometric andbiomechanical measures based on the computational simulations, whereinthe material properties are assigned to the 3D reconstructed surface ofthe vessel lumen based on invasive or non-invasive imaging, and whereinthe assigning of the material properties to the 3D reconstructed surfaceof the vessel lumen based on invasive or non-invasive imaging includes:determining wall or plaque thickness, lumen area, plaque eccentricityand plaque constituents based on invasive or non-invasive imaging.